Source code for brainpy._src.losses.regularization

# -*- coding: utf-8 -*-

from jax.tree_util import tree_flatten, tree_map

import jax.numpy as jnp
import brainpy.math as bm
from .utils import _is_leaf, _multi_return

__all__ = [

[docs] def l2_norm(x, axis=None): """Computes the L2 loss. Args: x: n-dimensional tensor of floats. Returns: scalar tensor containing the l2 loss of x. """ leaves, _ = tree_flatten(x) return jnp.sqrt(jnp.sum(jnp.asarray([jnp.vdot(x, x) for x in leaves]), axis=axis))
[docs] def mean_absolute(outputs, axis=None): r"""Computes the mean absolute error between x and y. Returns: tensor of shape (d_i, ..., for i in keep_axis) containing the mean absolute error. """ r = tree_map(lambda a: bm.mean(bm.abs(a), axis=axis), outputs, is_leaf=_is_leaf) return _multi_return(r)
[docs] def mean_square(predicts, axis=None): r = tree_map(lambda a: bm.mean(a ** 2, axis=axis), predicts, is_leaf=_is_leaf) return _multi_return(r)
[docs] def log_cosh(errors): r"""Calculates the log-cosh loss for a set of predictions. log(cosh(x)) is approximately `(x**2) / 2` for small x and `abs(x) - log(2)` for large x. It is a twice differentiable alternative to the Huber loss. References: [Chen et al, 2019]( Args: errors: a vector of arbitrary shape. Returns: the log-cosh loss. """ r = tree_map(lambda a: bm.logaddexp(a, -a) - bm.log(2.0).astype(a.dtype), errors, is_leaf=_is_leaf) return _multi_return(r)
[docs] def smooth_labels(labels, alpha: float) -> jnp.ndarray: r"""Apply label smoothing. Label smoothing is often used in combination with a cross-entropy loss. Smoothed labels favour small logit gaps, and it has been shown that this can provide better model calibration by preventing overconfident predictions. References: [Müller et al, 2019]( Args: labels: one hot labels to be smoothed. alpha: the smoothing factor, the greedy category with be assigned probability `(1-alpha) + alpha / num_categories` Returns: a smoothed version of the one hot input labels. """ r = tree_map(lambda tar: (1.0 - alpha) * tar + alpha / tar.shape[-1], labels, is_leaf=lambda x: isinstance(x, bm.Array)) return _multi_return(r)